Gingival enlargement is a clinical condition that has been widely studied and is directly associated with specific local or systemic conditions. Pregnancy has been presented to increase susceptibility to gingival inflammation. Sex hormones are believed to be a risk factor for periodontitis because of their ability to proliferate specific periodontal microorganisms and affect host immunological response, but it is unclear whether pregnancy gingivitis exposes or proceeds to periodontitis. In this case report, the patient reported with severe localised enlarged gingival mass which initiated when she was pregnant. After parturition, gingival enlargement was persisting and causing functional and aesthetic problem. Enlargement did not resolve even after non-surgical therapy; therefore, surgical excision of the entire enlarged gingival mass was preformed. Histopathological examination revealed capillary haemangioma. No evidence of malignancy was seen. No recurrence was seen even after 2 years of follow-up.

Biometric techniques are now helpful in identifying a person's identity, but criminals alter their look, behaviour, and psychological makeup to trick identification systems. We are employing a novel method called Deep Texture Features extraction from photos to solve this issue, followed by the construction of a machine learning model using the CNN (Convolution Neural Networks) algorithm. This method is also known as LBPNet or NLBPNet since it largely relies on the LBP (Local Binary Pattern) algorithm for features extraction. LBPNET, a machine learning convolution neural network, is the name of the network we created for this research to identify fraudulent face photographs. Here, we will first extract LBP from the photos before training the convolution neural network on the LBP descriptor images to create a training model. Every time we submit a new test picture, the training model will be applied to that test image to determine if it includes a false image or not. Details regarding LBP are shown below. keywords: Biometry, Identity, Recognition, Detection, Fake face.